Persistent memory and learning-loop skills for AI coding agents using mnemos MCP tools.

MITAuto-check passedAgent Workflows

Install Mnemos

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill mnemos -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace mnemos --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/community/mnemos/.claude/skills/mnemos .claude/skills/mnemos && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
mnemos
GitHub stars
2.8k
Token cost
~3.1k tokens
SKILL.md length
1,405 words
Files
1
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Persistent memory and learning-loop skills for AI coding agents using mnemos MCP tools.

  • Works in 3 steps: mnemos_ruminate_list → returns… → mnemos_ruminate_pack(id) → returns a… → Decide one of two outcomes
  • Mnemos MCP tools are available and you are doing work worth remembering
  • SKILL.md covers Overview, Prerequisites, Instructions and Output, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Mnemos is an agent skill from jeremylongshore/tons-of-skills-marketplace. Persistent memory and learning-loop skills for AI coding agents using mnemos MCP tools. Use whenever mnemos MCP tools are available and you are doing work worth remembering. Cross-session memory, conventions, corrections, and architectural decisions surface back into context automatically. Triggers on session start (record a goal), session end (record a summary), the user says "save", "remember", "record this", "we were wrong about", and on any genuine correction or architectural decision. Keeps mnemos's memory…

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Designed for Claude Code; the same MCP server also works in Claude Desktop, Cursor, Windsurf, and OpenAI Codex CLI. Requires the mnemos binary on PATH — run…

It sits in Agent Workflows, covering Agent memory, MCP servers and Session handoff. It works with Model Context Protocol. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Mnemos MCP tools are available and you are doing work worth remembering
  • Session start (record a goal)
  • Session end (record a summary)
  • The user says save

Example prompts

  • “remember”
  • “record this”
  • “we were wrong about”
  • “/mnemos”

Requirements

  • Compatibility (from SKILL.md): Designed for Claude Code; the same MCP server also works in Claude Desktop, Cursor, Windsurf, and OpenAI Codex CLI. Requires the mnemos binary on PATH — run mnemos init to wire the MCP server and mnemos doctor to verify.
  • Pre-approved tools (allowed-tools): mcp__mnemos

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. mnemos_ruminate_list → returns {candidates: [...], counts: {...}}. Pick one by ID.
  2. mnemos_ruminate_pack(id) → returns a review block with Hypothesis, Disconfirming evidence, Falsifiable restatement, Hostile review…
  3. Decide one of two outcomes

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • mcp__mnemos

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code; the same MCP server also works in Claude Desktop, Cursor, Windsurf, and OpenAI Codex CLI. Requires the mnemos binary on PATH — run mnemos init to wire the MCP server and mnemos doctor to verify.

    From compatibility in the SKILL.md frontmatter.

Context cost

Mnemos loads about 3.1k tokens when it runs. Until then it costs about 164 tokens; SKILL.md has 1,405 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~164
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 1,405 words, ~3,059 tokens.

Download SKILL.mdSave it as .claude/skills/mnemos/SKILL.md (or your agent's skills folder).
name
mnemos
description
Persistent memory and learning-loop skills for AI coding agents using mnemos MCP tools. Use whenever mnemos_* MCP tools are available and you are doing work worth remembering. Cross-session memory, conventions, corrections, and architectural decisions surface back into context automatically. Triggers on session start (record a goal), session end (record a summary), the user says "save", "remember", "record this", "we were wrong about", and on any genuine correction or architectural decision. Keeps mnemos's memory store alive across sessions so the next session starts smarter. Without this skill, agents silently edit and the store goes empty.
allowed-tools
mcp__mnemos
compatibility
Designed for Claude Code; the same MCP server also works in Claude Desktop, Cursor, Windsurf, and OpenAI Codex CLI. Requires the mnemos binary on PATH — run mnemos init to wire the MCP server and mnemos doctor to verify.
version
0.9.0
author
André Figueira <andre@polyxmedia.com>
license
MIT
tags
memory, mcp, persistent-memory, learning-loop, mnemos

mnemos

Overview

You have a persistent memory layer available via the mnemos_* MCP tools. By default you will skip these tools on plain editing tasks. Don't. Mnemos is the learning-loop primitive for the project you are working on: corrections, decisions, and conventions compound across sessions if you record them. Silently editing leaves the store empty and the next session blind.

There is a known correction already stored about this exact failure mode: "agent skipped mnemos_session_start on editing tasks — LLMs skip optional tool calls when the task looks like plain reading/editing; the agent needs an external nudge." This skill is that nudge.

Prerequisites

  • The mnemos binary is on PATH. mnemos doctor reports green for binary, config, storage, and each agent registration.
  • mnemos init has wired the MCP server into your agent host (Claude Code, Claude Desktop, Cursor, Windsurf, or Codex CLI).
  • The mnemos_* tools are visible this session. If they are not, the MCP server is not connected — see Error Handling.

Instructions

Session lifecycle

Start. The Claude Code SessionStart hook runs mnemos prewarm, which opens a mnemos session by default and injects a mnemos_session_id: line into context. If that ID is present, reuse it for session_id fields and do not call mnemos_session_start again. If no ID is present (manual MCP setup, non-Claude client, or hook disabled) and you are about to do real work, open one:

mnemos_session_start(
	project="<repo name from git or cwd>",
	goal="<one short line — what you are trying to do this session>"
)

Without a goal, the UserPromptSubmit hook backfills the first real prompt when available. With a goal, the session becomes a durable record that mnemos replay and future prewarms can use.

End. When the user signals done ("ship it", "that's it", "commit and close", "we're done"), close the session:

mnemos_session_end(
  session_id="<id>",
  summary="<one or two sentence recap of what shipped>",
  status="ok"
)

Status values: ok | failed | blocked | abandoned. An open session with no summary is dead weight in the next session's prewarm.

Which tool for which signal
Signal from the user or the workTool to call
About to execute a non-trivial plan (after deciding approach, before first edit)mnemos_premortem(plan, project) — returns how similar attempts failed; read it before proceeding
A non-obvious decision, pattern, or architectural call worth preservingmnemos_save with type=decision / architecture / pattern
User corrects an approach: "actually no, do X because Y"mnemos_correct(tried, wrong_because, fix)
A rule that should apply to the project forever: "always wrap errors with %w"mnemos_convention(title, rule, rationale, project)
A file you are editing heavily and will revisitmnemos_touch(path, project)
Reloading state mid-session after a context compactionmnemos_context(session_id=..., mode="recovery")
Checking history before doing something you might already have figured outmnemos_search(query, project)
Quick state check ("is mnemos actually recording?")mnemos_stats
Linking two observations so they surface togethermnemos_link(from_id, to_id, relation)
At the top of a long session, or when the user questions a stored rulemnemos_ruminate_list → pick one → mnemos_ruminate_pack
Replacing a weak skill or stale rule after hostile reviewmnemos_ruminate_resolve(id, resolved_by, why_better)
Leaving a rule intact because the evidence was noisemnemos_ruminate_dismiss(id, reason)
What not to do
  • Don't call mnemos_save for ephemeral within-session context ("I'm reading X now"). Save things worth remembering next session, not running commentary on this one.
  • Don't call mnemos_correct for trivial typos or one-off slips. Corrections are for conceptual mistakes that would repeat.
  • Don't fabricate tool arguments. Derive project from the git remote or the working directory; if unclear, ask.
  • Don't spam mnemos_search before every edit. It is fast but the goal is a richer memory, not a pre-check ritual.
  • Don't skip mnemos_session_end. Check for open sessions via mnemos_stats at the top of a session; if one is open from a past run, close it.
Rumination: when a stored rule looks wrong

Mnemos flags stored knowledge whose effectiveness has fallen below the threshold. These are rumination candidates and they want a hostile review, not a rubber stamp.

Trigger situations where you should check the queue:

  • At the top of any long or consequential session (run mnemos_ruminate_list).
  • When the user questions a rule the memory layer is surfacing ("is this still true?").
  • When you just watched a saved skill or convention fail in practice.

Workflow:

  1. mnemos_ruminate_list → returns {candidates: [...], counts: {...}}. Pick one by ID.
  2. mnemos_ruminate_pack(id) → returns a review block with Hypothesis, Disconfirming evidence, Falsifiable restatement, Hostile review prompts, and an Action section. Read the block. Answer the hostile prompts honestly. The prompts are ordered: steel-man the opposite → find the fatal flaw → decide falsification vs noise → check whether context shifted → state the new prediction.
  3. Decide one of two outcomes:
    • Resolve with a concrete revision: mnemos_ruminate_resolve(id, resolved_by, why_better). resolved_by is the ID of the new skill version or superseding observation. why_better must be a sentence naming a new prediction the revision makes that the old version did not — Popper's falsifiability guard. Cosmetic rewording is rejected by the server (min 16 chars, but length is not the real test — the test is whether you can state a new prediction).
    • Dismiss with an honest reason: mnemos_ruminate_dismiss(id, reason). Use when the hostile review convinced you the rule stands and the evidence was a one-off. The reason is preserved so a future rumination pass doesn't re-raise the same flag without context.

Never close a candidate silently or with filler. A rumination that resolves to "no change" is either a dismissal with a real reason or a bug in the monitor — both belong in the provenance trail, not in the void.

Show full SKILL.md (539 more words)Show less
Quick self-check

Before wrapping any multi-step session, run through this:

  1. Do I have a mnemos_session_id from the prewarm hook or mnemos_session_start? If not, call mnemos_session_start now and cite what the goal was retroactively.
  2. Is there a correction from this session? If the user said "no, do X instead", record it before they have to ask.
  3. Is there a decision worth preserving? If the answer took thinking to arrive at, save it.
  4. Did I check mnemos_ruminate_list at least once this session? If the queue had pending candidates on a topic I touched, I should have resolved or dismissed them.
  5. Did I close the session with a summary that future-me can read in a prewarm?

Output

  • mnemos_session_start returns a session_id plus a pre-warmed context block (conventions, recent sessions, matching skills, corrections, hot files) already token-budgeted for injection.
  • mnemos_save / mnemos_correct / mnemos_convention persist a structured observation and return its ID. Corrections store tried / wrong_because / fix as structured fields.
  • Three corrections that cluster on the same (agent, project, label) are auto-promoted into a skill by the deterministic dream pass — no LLM in the loop, so the same corrections always yield the same skill.
  • mnemos_search / mnemos_context return ranked observations with provenance (source_kind, trust_tier); raw-tier (quarantined) content is excluded unless include_raw=true.
  • mnemos_stats returns counts, top tags, and recent sessions so you can confirm the store is actually recording.

Error Handling

  • mnemos_* tools are not visible. The MCP server is not connected. Tell the user to run mnemos doctor; if a client shows red, mnemos init re-wires it. Do not silently proceed without memory — that is the failure mode this skill exists to prevent.
  • No mnemos_session_id in context. The SessionStart hook did not run (non-Claude client or hook disabled). Call mnemos_session_start before doing real work.
  • An open session from a past run. mnemos_stats surfaces it. Close it with mnemos_session_end(..., status="abandoned") before opening a new one, or it pollutes the next prewarm.
  • A save is rejected with a [MNEMOS: FLAGGED] / safety error. The write-boundary scanner caught a prompt-injection pattern in the content. Do not retry verbatim — strip the suspicious payload or save only the genuine, user-authored signal.
  • A mnemos_ruminate_resolve is rejected. The why_better did not name a new prediction (Popper guard). Rewrite it to state what the revision predicts that the old version did not.

Examples

Correction shape

Corrections are the atomic unit of the mnemos learning loop. Fill the fields honestly:

json
{
  "title": "oauth retry without backoff",
  "tried": "retry on 401",
  "wrong_because": "401 is auth failure, not transient",
  "fix": "refresh token, then retry once",
  "trigger_context": "implementing token refresh for the apollo integration",
  "project": "<repo>"
}

trigger_context is optional but valuable: it populates the ## When this applies section of the auto-promoted skill if three corrections cluster on the same label.

Save shape
json
{
  "title": "use modernc.org/sqlite, not mattn/go-sqlite3",
  "content": "pure-Go driver keeps the binary CGO-free and cross-compilable to linux/darwin/windows without a toolchain on the install path",
  "type": "decision",
  "rationale": "zero-CGO is a hard invariant",
  "project": "mnemos",
  "tags": ["sqlite", "build", "dependencies"],
  "importance": 8
}

Valid type values: decision | bugfix | pattern | preference | context | architecture | episodic | semantic | procedural | correction | convention.

Correction-to-skill loop

Record the same class of mistake three times across sessions:

mnemos_correct(tried="retry on 401", wrong_because="401 is auth, not transient", fix="refresh token then retry once", project="api", tags=["oauth"])
# ...two more oauth corrections in later sessions...

The next dream pass clusters the three oauth corrections and promotes a skill with ## When this applies / ## Avoid / ## Do sections. From then on it surfaces in prewarm before you touch that code path again.

Resources

  • docs/MCP_TOOLS.md — full reference for every mnemos_* tool and its arguments.
  • docs/ARCHITECTURE.md — how prewarm, the dream pass, ranking, and provenance fit together.
  • docs/QUICKSTART.md — first-run setup and the install path.
  • README.md — the efficacy harness (mnemos verify) and the measured capture/behavior numbers.
  • mnemos doctor — live check that the binary, config, storage, and each agent registration are healthy.

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/community/mnemos/.claude/skills/mnemos of jeremylongshore/tons-of-skills-marketplace.

Open the folder on GitHubat commit cfae287

Compare with similar skills

Mnemos next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Mnemos compared with similar skills
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Mnemos this skilljeremylongshore/tons-of-skills-marketplace2.8k—~3.1kAutomated safety check: PassMIT
Memori MCP Memory UsageMemoriLabs/Memori17k—~3.8kAutomated safety check: PassMIT
Engram MemoryPatdolitse/piia-engram163—~1.3kAutomated safety check: PassAGPL-3.0-or-later
Prism Startup Contextdcostenco/prism-coder158—~1.4kAutomated safety check: PassApache-2.0
MemPalace Setup and OperationMemPalace/mempalace60k—~2.2kAutomated safety check: PassMIT
agentmemory Setup and Diagnosticsrohitg00/agentmemory29k—~1kAutomated safety check: NotesApache-2.0

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Categories

Questions about Mnemos

What does Mnemos do?

Persistent memory and learning-loop skills for AI coding agents using mnemos MCP tools. Mnemos is an agent skill from jeremylongshore/tons-of-skills-marketplace. Persistent memory and learning-loop skills for AI coding agents using mnemos MCP tools.

When should I use Mnemos?

Mnemos fits situations like: mnemos MCP tools are available and you are doing work worth remembering; session start (record a goal); session end (record a summary); the user says save.

How do I install Mnemos in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill mnemos -a claude-code`. Or copy the skill folder (plugins/community/mnemos/.claude/skills/mnemos in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/mnemos in your project. Claude Code loads it when a task matches its description.

How do I install Mnemos in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill mnemos -a codex`. Or copy the skill folder (plugins/community/mnemos/.claude/skills/mnemos in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/mnemos in your project. Codex loads it when a task matches its description.

Can I use Mnemos in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill mnemos -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mnemos, .gemini/skills/mnemos, .github/skills/mnemos and .opencode/skills/mnemos in your project.

What does Mnemos need to run?

SKILL.md names no scripts, command-line tools or credentials: Mnemos is instructions for the agent only. Its frontmatter pre-approves these tools: mcp__mnemos. Compatibility (from SKILL.md): Designed for Claude Code; the same MCP server also works in Claude Desktop, Cursor, Windsurf, and OpenAI Codex CLI. Requires the mnemos binary on PATH — run mnemos init to wire the MCP server and mnemos doctor to verify..

Does Mnemos access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Mnemos safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Mnemos use?

Mnemos is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Mnemos use?

About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Mnemos?

Skills that share tags, products or a category with Mnemos: Memori MCP Memory Usage (MemoriLabs/Memori, 17k stars), Engram Memory (Patdolitse/piia-engram, 163 stars), Prism Startup Context (dcostenco/prism-coder, 158 stars) and MemPalace Setup and Operation (MemPalace/mempalace, 60k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mnemos?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.